Intraocular lenses that improve post-surgical spectacle independent and methods of manufacturing thereof

ABSTRACT

A Bayesian model for predicting spectacle independence of one or more IOLs based on pre-clinical data (e.g., visual acuity value for one or more defocus values) of an IOL. The Bayesian model is trained to assign appropriate weights for different combinations of defocus values.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a divisional of and claims priority to U.S. patentapplication Ser. No. 16/205,206, filed Nov. 29, 2018, which claims thebenefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent ApplicationNo. 62/593,162, filed Nov. 30, 2017, which is incorporated herein byreference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

This application is related to systems and methods of selecting,designing and manufacturing intraocular lenses that improvepost-surgical spectacle independence in cataract patients.

Description of the Related Art

Retinal image quality of intraocular lenses when implanted in the eye ofa patient can be estimated from different metrics obtained frompre-clinical measurements. For example, through focus visual acuity forpsuedophakic patients can be predicted from metrics based on variouspre-clinical measurements. However, many of the metrics used to predictpost-surgical optical performance of intraocular lenses are unable toreliably predict spectacle independence for psuedophakic patients.Spectacle independence is a desired outcome following cataract surgeryfor most patients. Accordingly, it would be desirable to develop newtechniques to reliably predict the spectacle independence forpseudophakic patients receiving different IOLs.

SUMMARY OF THE INVENTION

This application contemplates systems and methods of predictingspectacle independence utilizing pre-clinical data to simulate andpredict the expected percentage of patients that will be spectacleindependent following cataract surgery with an IOL in which pre-clinical(measured or simulated) data is available. One piece of pre-clinicaldata that can be used to predict spectacle independence is through-focusvisual acuity at one or more defocus positions. For example, it may bepossible to predict spectacle independence based on through-focus visualacuity at near distances, such as, for example, near distances greaterthan or equal to about 25 cm and less than or equal to about 50 cm.However, there is limited information on whether a high peak inthrough-focus visual acuity at one near distance (e.g., 40 cm) is abetter predictor of spectacle independence or whether a flat but lowerthrough-focus visual acuity at a plurality of near distance valuesbetween about 25 cm and about 50 cm is a better predictor of spectacleindependence. The methods and systems contemplated in this applicationare based on applying Bayesian models to an initial data set includingknown spectacle independence information obtained from clinical studiesgathering responses to questions in a questionnaire from differentpatients implanted with different intraocular lenses for which measuredor simulated pre-clinical data is available and calculating theprobability that a patient would be spectacle independent for a certainvalue of visual acuity at a certain defocus distance. Since the initialdata set is based on a small number of patients (e.g., less than orequal to about 500, less than or equal to about 1000, or less than orequal to about 2000), the prediction of spectacle independence fromthrough-focus visual acuity values at one or more defocus positions canbe calculated using “medium data” solutions that operate on medium sizeddatabases. Additionally, machine learning can be employed toappropriately weight and scale the contribution of through-focus visualacuity performance at various defocus distances to spectacleindependence.

One innovative aspect of the subject matter disclosed herein isimplemented in an optical system configured to select an intraocularlens (IOL) from a plurality of IOLs for manufacture or for implantationinto a patient eye, the selected IOL configured or to be manufactured toimprove post-surgical spectacle independence outcome for the patient.The optical system comprises a processor configured to executeprogrammable instructions stored in a non-transitory computer storagemedium; and a population database comprising clinical data for aplurality of patients less than or equal to about 5000 implanted withone of the plurality of IOLs, the clinical data comprising informationrelated to spectacle independence for a plurality of values of visualacuity between about −0.2 log MAR and about 1 log MAR at various defocusconditions between about −5D and 0D, wherein the information related tospectacle independence is based on responses of the patients implantedwith one of the plurality of IOLs to a questionnaire. The processor isconfigured to calculate for each of the plurality of IOLs, a probabilityof being spectacle independent for visual acuity equal to a thresholdvalue between about −0.2 log MAR and about 1 log MAR at at least onedefocus conditions between about −5D and 0D based on the informationrelated to spectacle independence obtained from the population database;and identify one of the plurality of IOLs having a higher probability ofbeing spectacle independent for manufacture or for implantation into thepatient's eye.

The processor can be further configured to calculate for each of theplurality of IOLs, a probability of being spectacle independent forvisual acuity equal to a threshold value between about −0.2 log MAR andabout 1 log MAR at at least two or more defocus conditions between about−5D and 0D. The processor can be further configured to assign a weightto the probability of being spectacle independent for visual acuityequal to a threshold value between about −0.2 log MAR and about 1 logMAR at at least two or more defocus conditions between about −5D and 0D.The processor can be configured to execute a machine learning algorithmto determine the weight.

Another innovative aspect of the subject matter disclosed herein can beembodied in an optical system configured to identify an intraocular lens(IOL) that will improve post-surgical spectacle independence. The systemcomprises a processor configured to execute programmable instructionsstored in a non-transitory computer storage medium to calculate aprobability of achieving spectacle independence for at least two IOLsbased on clinical data providing visual acuity at a first defocusposition for the at least two IOLs in a population of patients implantedwith one of the at least two IOLs; and identify one of the at least twoIOLs having higher probability of achieving spectacle independence.

The processor can be further configured to calculate the probability ofachieving spectacle independence for at least two IOLs based on at leastone of: clinical data providing visual acuity at a second defocusposition for the at least two IOLs in the population; standard deviationof pre-clinical visual acuity for the at least two IOLs at the first orthe second defocus positions; clinical data providing minimum readableprint size in mm in the population; modulation transfer function (MTF)at one or more frequencies at different distances for different pupilsizes; or area under the modulation transfer function at one or morefrequencies at different distances for different pupil sizes.

The first defocus position can be about −2.5D corresponding to adistance of about 40 cm. The second defocus position can have a valuebetween about −5D and about −0.5D. A size of the population can be lessthan about 1000. The processor can be configured to execute programmableinstructions stored in a non-transitory computer storage medium totransmit one or more parameters of the identified IOL to a displaydevice or an IOL manufacturing system.

Yet another innovative aspect of the subject matter disclosed herein isimplemented in a system for predicting post-surgical spectacleindependence of one or more IOLs, the system comprising a processorconfigured to execute programmable instructions stored in anon-transitory computer storage medium to calculate a probability ofachieving spectacle independence for the one or more IOLs based onclinical data providing visual acuity at one or more defocus positionfor the one or more IOLs in a population of patients implanted with oneof the one or more IOLs.

The processor can be further configured to execute programmableinstructions stored in a non-transitory computer storage medium tocalculate the probability of achieving spectacle independent based onone or more combinations of visual acuity at at least two or moredefocus positions for the one or more IOLs in the population. Theprocessor can be further configured to execute programmable instructionsstored in a non-transitory computer storage medium to assign weights tothe one or more combinations of visual acuity. The weights can bedetermined based on a machine learning algorithm.

An innovative aspect of the subject matter disclosed herein isimplemented in a method of manufacturing an IOL comprising: receivingone or more parameters of an IOL selected from a plurality of IOLs basedon calculating a probability of achieving spectacle independence for theplurality of IOLs from clinical data providing visual acuity at one ormore defocus position for the plurality of IOLs in a population ofpatients implanted with one of the plurality of IOLs; and manufacturingthe selected IOL.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention may be better understood from thefollowing detailed description when read in conjunction with theaccompanying drawings. Such embodiments, which are for illustrativepurposes only, depict novel and non-obvious aspects of the invention.The drawings include the following figures:

FIG. 1 is a flowchart illustrating a method of predicting values usingBayesian analysis.

FIG. 2 shows an example method of calculating probability of an eventusing Bayesian statistics.

FIG. 3A is data from clinical studies for 162 pseudophakic patients thatare spectacle independent. FIG. 3B is data from clinical studies for 159pseudophakic patients that are not spectacle independent.

FIG. 4 is a graph showing a predicted percentage of patients who arespectacle independent obtained using a simple model based on neardistance visual acuity.

FIG. 5 illustrates the predicted spectacle independence based onpre-clinical data for different implementations of intraocular lenses.

FIG. 6 is a graphical representation of the elements of computing systemfor designing or selecting an ophthalmic lens.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Each and every feature described herein, and each and every combinationof two or more of such features, is included within the scope of thepresent invention provided that the features included in such acombination are not mutually inconsistent.

As used herein, the terms “about” or “approximately”, when used inreference to a Diopter value of an optical power, mean within plus orminus 0.25 Diopter of the referenced optical power(s). As used herein,the terms “about” or “approximately”, when used in reference to apercentage (%), mean within plus or minus one percent (±1%). As usedherein, the terms “about” or “approximately”, when used in reference toa linear dimension (e.g., length, width, thickness, distance, etc.) meanwithin plus or minus one percent (1%) of the value of the referencedlinear dimension.

Spectacle independence is a highly desired outcome following cataractsurgery. It is possible to predict through-focus visual acuity (VA) fordifferent implementations of intraocular lenses (IOLs) based onpre-clinical data using mathematical models. Through-focus VA can bepredicted for one or more defocus values based on available pre-clinicaldata for an IOL including but not limited to IOL characteristics such asrefractive index of the IOL, radii of curvature, diffraction power,diffraction step height, transition zones and IOL thickness. Thesecharacteristics can be used in a ray tracing simulation software topredict through-focus MTF, which can predict through-focus VA. IOLdesigns can be optimized to achieve a desired optical performance basedon the predicted values of through-focus VA.

The predicted through-focus VA at one or more defocus values can bebased on an output generated by an electronic processor from availablepre-clinical data of the IOL input to the electronic processor. Theelectronic processor can be configured to execute instructions stored ona non-transitory hardware storage medium to generate the output. Anexample electronic processing system is discussed in detail below withreference to FIG. 6. The through-focus VA at one or more defocus valuesfor an IOL can be measured using a Log MAR chart that can comprise rowsof letters. The through-focus VA of an implementation of an IOL is 0 LogMAR if the implementation of the IOL can resolve details as small as 1minute of visual angle. A series of negative powered lenses can beplaced in front of the IOL to simulate near distance vision. In thismanner through-focus VA at a plurality of defocus values can be measuredto obtain a defocus curve. Without any loss of generality, a defocusvalue of −2.5 Diopters can correspond to a near distance value of about40 cm. Defocus values less than −2.5 Diopters correspond to neardistance values less than about 40 cm.

This application contemplates systems and methods to predict theexpected percentage of patients that will be spectacle independent basedwhen implanted with an IOL whose pre-clinical data is available. Thespectacle independence can be estimated using Bayesian analysis.Bayesian analysis is a statistical procedure which combines priordistribution of one or more population parameters before any data isobserved with observed information in a sample to obtain an updatedprobability distribution for the one or more parameters. FIG. 1illustrates an implementation of the Bayesian analysis. As shown in FIG.1, the Bayesian analysis begins with a starting model 101. The startingmodel can be a prior probability density function (pdf) of differenthypotheses associated with certain probabilities of being true. New datais collected from a sample of the population as shown in block 103. Thenew data can be conditional on the different hypotheses. The pdf of thedifferent hypotheses is updated based on the prior pdf and the new datausing Bayes' rule shown in block 102. Mathematical Bayes' rule is givenby the equation

$\left. {{P\left( A \right.}B} \right) = \frac{\left. {{p\left( B \right.}A} \right)*{P(A)}}{P(B)}$

When using Bayes analysis to estimate spectacle independence frompre-clinical data, A can correspond to the pdf of different percentagesof spectacle independence, and B can correspond to the clinical datathat is used to predict spectacle independence.

The clinical data can be a singular value or a multidimensional value.Through-focus VA is an example of a multidimensional value (for example,VA at −3 D, at −2.5 D, at −2 D, . . . , at 0 D of defocus). A singularvalue can also be used to predict the percentage of spectacleindependence. Predicting spectacle independence based on a singularvalue can be simple and computationally less intensive. Singular valuesused for predicting spectacle independence can include (i) VA at neardistance (e.g., 40 cm), (ii) VA at any other distance, (iii) standarddeviation of VA in a certain distance/defocus range which can be ameasure of the variability/consistency of VA in the distance/defocusrange, (iv) minimum readable print size in mm calculated by predictedangular VA which is converted to stroke width of letters in mm at thatdistance and taking the minimum value. This corresponds to best distanceat which a patient can view small print, (v) modulation transferfunction (MTF) at certain spatial frequencies at certain distances andpupil sizes, or (vi) Area under MTF curve at certain distances and pupilsizes.

For singular value metrics B, A can comprise a plurality ofprobabilities i of spectacle independence, such as 1%, 2%, 3%, . . . ,99%, 100%. The conditional probability of P(AIi|B) can be calculatedusing Bayes' rule by the equation

$\left. {{P\left( {A\_ i} \right.}B} \right) = \frac{\left. {{p\left( B \right.}{A\_ i}} \right)*{P({A\_ i})}}{P(B)}$

The plurality of probabilities of different percentages of spectacleindependence P(A_i) can be determined based on a prior model ofspectacle independence, such as having a linear function between 5% and95%, in the VA range from 0.6 Log MAR to 0 Log MAR at a certain defocusvalue (e.g., −2.5 D). Bayes analysis can then be used to estimate theprobability P(B|A_i), the probability of the set given clinical dataassuming spectacle independence A_i through direct calculation from theclinical data as well as the model. P(B) can be considered as anormalization factor.

The method discussed above can be applied for multidimensional values aswell. However, some modification and additional techniques may berequired when the multidimensional value metric is through-focus VA atdifferent defocus values, since VA at different defocus values may becorrelated. Due to the relatively large number of defocus positions,correcting for interaction effects may not possible. In someimplementations of Bayesian analysis that employs through-focus VA atdifferent defocus values as the pre-clinical metric, a multidimensionalmatrix including all possible combinations of VA values at differentdefocus values may be generated. This matrix can be sampled, forexample, in steps of 0.5 D and 0.1 Log MAR. At each such combinationthere is a pdf for different percentages of spectacle independence. Thedata added into the matrix could be additive for any VA at any valuehigher than the given curve for, thus phrasing the probabilities as“having VA of x or higher”.

This method is illustrated in FIG. 2 which has a sample of 20 subjects.Five of the 20 subjects are spectacle independent as shown in block 201and the remaining fifteen wear spectacles as shown in block 202. Thus,the probability of spectacle independence P(SI) is equal to 5/20 or 25%.Of the five subjects who are spectacle independent, three have a visualacuity greater than 0.1 as shown in block 203 while two have a visualacuity less than 0.1 as shown in block 204. Thus, the conditionalprobability of having visual acuity greater than 0.1 when beingspectacle independent P(VA>0.1|SI) is equal to ⅗ or 60%. Four of the 20subjects have visual acuity greater than 0.1 as shown in block 205 whilesixteen of the 20 subjects have visual acuity less than 0.1 as shown inblock 206. Thus, the probability that visual acuity is greater than 0.1P(VA>0.1) is equal to 4/20 or 20%. Three subjects having visual acuitygreater than 0.1 are spectacle independent as shown in block 207 while 1subject having visual acuity greater than 0.1 is not spectacleindependent as shown in block 208. Thus, the probability of beingspectacle independent given visual acuity is greater than 0.1P(SI|VA>0.1) is equal to ¾ or 75%. The probability of being spectacleindependent given visual acuity is greater than 0.1 P(SI|VA>0.1) canalso be calculated using Bayes' rule as

$\left. {{{P\left( {SI} \right.}{VA}} > 0.1} \right) = \frac{\left. {{P\left( {{VA} > 0.1} \right.}{SI}} \right)*{P({SI})}}{P\left( {{VA} > 0.1} \right)}$

which is equal to 75%.

Another example to illustrate the method of determining spectacleindependence based on visual acuity is described below. For the sake ofsimplicity a singular value metric is used to estimate spectacleindependence, but the same techniques can be generalized when amultidimensional value is used.

Consider that it is desired to investigate the probability of beingspectacle independent if VA at −2.5 D is −0.05 Log MAR. From a clinicaldata set obtained from observation of 321 subjects, it is found thatthere are four subjects who are not spectacle independent and have VA at−2.5 D greater than or equal to −0.05 and there are 16 subjects who arespectacle independent and have VA at −2.5 D greater than or equal to−0.05. From the clinical data set, it is further observed that there are155 subjects who are not spectacle independent and have VA at −2.5 Dless than −0.05 and 146 subjects who are spectacle independent and haveVA at −2.5 D less than −0.05. FIG. 3A shows the defocus curve forspectacle independent subjects and FIG. 3B shows the defocus curve forsubjects who wear spectacles.

Based on the information, the probability of being spectacle independentwhen VA at −2.5D is greater than or equal to −0.05 P(SI|VA at−2.5D>−0.05)=P(VA at −2.5D>−0.05|SI)*P(SI)/P(VA at −2.5D>−0.05) which isequal to ( 16/162)*(162/321)/( 20/321) which is equal to 80%.

FIG. 4 shows the percentage of spectacle independence for differentvalues of near distance VA obtained using a singular value as describedabove. It is noted from FIG. 4 that using only near distance VA as apredictor for spectacle independence has an 80% of achieving spectacleindependence, regardless of the value of near distance VA of the lens.

It is further noted from FIG. 4 that if VA at all distances is low thenthe probability of being spectacle independent is about 50%. However, inthe clinical data set, there were 102 subjects who are not spectacleindependent and have a VA of 0.3 Log MAR or worse, 56 subjects who arenot spectacle independent and have VA better than 0.3 Log MAR, 13subjects who are spectacle independent VA of 0.3 Log MAR or worse, and144 subjects who are spectacle independent and have VA better than 0.3Log MAR.

Using Bayes analysis, the probability of being spectacle dependent andhaving a VA of 0.3 Log MAR or worse P(SD|VA of 0.3 Log MAR orworse)=P(VA of 0.3 Log MAR or worse|SD)*P(SD)/P(VA of 0.3 Log MAR orworse) which is equal to (102/158)*(158/315)/(115/315)=88.7%. Thus,there is an 11.3% chance of being spectacle independent if the VA is 0.3Log MAR or worse. Thus, the model of predicting spectacle independencebased on singular VA value can be updated by combining the two estimatesto better predict the chance of being spectacle independent given acertain VA value as described below.

Consider a vector t of length 100 with the probabilities of having 1%,2%, 3% . . . , 99%, 100% spectacle independent at a certain value of VA.The vector t can be updated according to the example below.

Consider that the vector t has a length of 2 with a 0.5 probability of80% being spectacle independent and 0.5 probability of 70% beingspectacle independent. For VA values above −0.05 we have 4 subjects whowear spectacles and 16 subjects who don't wear spectacles. Theprobability of being spectacle independent for VA above −0.05, can becalculated using the P(x)=(N!/(x! (N−x)!))*(t∧x)*(1−t)∧(N−x) where N istotal number, x is the number of spectacle independent and t is theprobability. For the example above, N=20, x=16 and t=0.7 and 0.8.Accordingly, P(x) is equal to 0.13 for t=0.7 and 0.21 for t=0.8. If theinitial prior pdf P(A) is [0.5, 0.5], and P(B) is applied as a standardnormalization factor, P(A|B)=[0.5*0.13,0.5*0.21]/(0.5*0.13+0.5*0.21)=0.38 for t=0.7, and 0.62 for t=0.8. Inthis manner the vector t is updated. A similar technique can be appliedfor estimating spectacle independence for VA worse than a certain value,and the results combined using a range of methods. A skilled personwould understand that it is advantageous to start with a reasonableprior pdf as the posterior probability distribution can skew towards theprior pdf when the number of subjects is low.

The abovementioned technique can also be applied to themulti-dimensional case, where a larger matrix is used and a combinationof VA applicable to all defocus positions is selected. In such a case,the sampling may be limited. The sampling limitation can be overcome byusing a two-step process, wherein first a coarse sampling is applied,e.g. steps of 0.1 Log MAR. Thereafter, if the VA of interest to test ise.g. 0.12, we combine the two nearby steps, with 80% weight to estimatesfor VA=0.1 and 20% weight to the estimate with VA=0.2.

The Bayesian analysis method can be expanded to cover more than a binaryoutcome of spectacle dependent/spectacle independent, and insteaddescribe the probability of never wearing spectacles, of wearingspectacles a little bit of the time, some of the time, or all of thetime.

The Bayesian analysis method can be expanded to incorporate othercharacteristics of the patients, such as age, gender, eye length, pupilsize, ethnicity, corneal aberrations, life style or combinationsthereof.

The Bayesian analysis method of estimating spectacle independence fordifferent parameters can be incorporated in an IOL design and/ormanufacturing process. The parameter space of IOL design allowsvariation of IOL characteristics such as radii of curvature, diffractionpower, diffraction step height, transition zones and IOL thickness.These characteristics can be used in a ray tracing simulation softwareto predict through focus MTF, which can predict VA. Using Bayesiananalysis, the probability of spectacle independence can be calculated,and the IOL characteristics optimized such that the highest possiblespectacle independence is achieved, in conjunction with other simulatedand desired constraints such as distance image quality. Bayesiananalysis can also be used to predict how suitable certain treatmenttechniques, such as making the patients slightly myopic postoperativelycan positively affect spectacle independence. Bayesian analysis toestimate spectacle independence can also be used to select an IOL forimplantation in a patient that would increase the chance of the patientto be spectacle independent for a variety of tasks such as reading,viewing a smartphone, computer use or combinations thereof.

The spectacle independence of five different implementations of IOLs waspredicted based on pre-clinical data based on the Bayesian analysismethod described above. To predict spectacle independence, a data set of321 patients from three different studies was used. The patients werebilaterally implanted with five different implementations of IOLs.Spectacle independence was coded as a binary outcome. Through focus VAwas varied in steps of 0.5D between −3D and 0D. A Bayesian model toestimate rate of spectacle independence was developed. The Bayesianmodel was configured to calculate probability of spectacle independencefor VA better than a certain value as well as probability of spectacledependence for VA worse than a certain value. The Bayesian model wasfurther configured to calculate probability with different combinationsof VA at different defocus values. For example, the Bayesian model wasconfigured to (i) calculate probability of VA greater than or worse thana certain value for different single defocus values (e.g., 0D, −0.5D,−1D, −1.5D, −2D, −2.5D, −3D), (ii) calculate probability of VA greaterthan or worse than a certain value for combinations of two differentdefocus values (e.g., −3D and −2.5D, −2D and −1D), and (iii) calculateprobability of VA greater than or worse than a certain value forcombinations of three or more different defocus values. For example, themodel was configured to calculate probability of VA greater than orworse than a certain value for combination of seven different defocusvalues (e.g., 0D, −0.5D, −1D, −1.5D, −2D, −2.5D, and −3D).

The model was trained to combine and weight the different probabilitiesin order to have outcomes closest to the reported rates of spectacleindependence. For example, probability of VA greater than or worse thana certain value for combination of two or more different defocus valuesthat are closer to each other was assigned a higher weight thanprobability of VA greater than or worse than a certain value forcombination of two or more different defocus values that are fartherfrom each other. As another example, probability of VA greater than orworse than a certain value for different defocus values corresponding tonear distances between 25 cm and about 40 cm can be assigned a higherweight than VA at other defocus values.

The table below shows the clinically measured percentage spectacleindependence for five different IOL implementations Lens 1, Lens 2, Lens3, Lens 4 and Lens 5. The predicted percentage of spectacle independenceusing a Bayesian model with multidimensional values as described hereinas well as a single through-focus VA at one defocus value is also shownin the table below. The average error and the r∧2 values for thedifferent Bayesian models are also included in the table below.

Lens Lens Lens Lens Lens 1 2 3 4 5 Error r{circumflex over ( )}2Clinical 93% 76% 66% 62%  1% Bayesian Model 95% 70% 74% 51%  2%  5% 0.96with VA at a plurality of defocus values Bayesian Model 87% 71% 59% 36%15% 12% 0.84 with VA at defocus value of −3D only Bayesian Model 71% 73%66% 38% 13% 12% 0.83 with VA at defocus value −2.5D only Bayesian Model37% 63% 69% 50% 12% 19% 0.45 with VA at defocus value −2D only BayesianModel 36% 44% 60% 59% 34% 26% 0.07 with VA at defocus value −1.5D only

It is noted from the table above that the Bayesian model based onthrough-focus VA at a plurality of defocus values as described hereinhad the highest degree of correlation (r∧2 of 0.96 with the clinicallymeasured spectacle independence.

The benefit of inducing 0.5D of myopia for mini-monovision can also beevaluated using the through focus VA predicted from pre-clinicalmethods. FIG. 5 shows the through-focus VA based on pre-clinical datafor an implementation of an IOL (curve 502) and the same curved shiftedby 0.5D (curve 501). Using the Bayesian model discussed herein, it wasestimated that an extended range of vision IOL with a spectacleindependence rate of 62% could have that rate increased to 83.2% if thepatients were made 0.5D myopic.

Referring to FIG. 6, in certain embodiments, a computer system 600 forestimating the probability of being spectacle independent based onavailable or measured pre-clinical data for an IOL comprises anelectronic processor 602 and a computer readable memory 604 coupled tothe processor 602. The computer readable memory 604 has stored thereinan array of ordered values 608 and sequences of instructions 610 which,when executed by the processor 602, cause the processor 602 to performcertain functions or execute certain modules. For example, a module canbe executed that is configured to calculate spectacle independence forone or more IOLs. As another example, a module can be executed that isconfigured to perform the Bayesian analysis discussed herein and selectan IOL that has the highest probability of being spectacle independent.As another example, a module can be executed that is configured todetermine an improved or optimal IOL design that improves theprobability of being spectacle independent.

The array of ordered values 608 may comprise, for example, one or moreocular dimensions of an eye or plurality of eyes from a database, adesired refractive outcome, parameters of an eye model based on one ormore characteristics of at least one eye, and data related to an IOL orset of IOLs such as a power, clinical data providing the number ofsubjects who are spectacle dependent at one or more VA values, and/orclinical data providing the number of subjects who are spectacleindependent at one or more VA values. In some embodiments, the sequenceof instructions 610 includes variation of IOL characteristics such asradii of curvature, diffraction power, diffraction step height,transition zones and IOL thickness, using these characteristics in a raytracing simulation software to predict through-focus VA, using Bayesiananalysis to predict the probability of spectacle independence, optimizeIOL characteristics to increase spectacle independence or select an IOLhaving the highest probability of spectacle independence.

The computer system 600 may be a general purpose desktop or laptopcomputer or may comprise hardware specifically configured performing thedesired calculations. In some embodiments, the computer system 600 isconfigured to be electronically coupled to another device such as aphacoemulsification console or one or more instruments for obtainingmeasurements of an eye or a plurality of eyes. In other embodiments, thecomputer system 600 is a handheld device that may be adapted to beelectronically coupled to one of the devices just listed. In yet otherembodiments, the computer system 600 is, or is part of, refractiveplanner configured to provide one or more suitable intraocular lensesfor implantation based on physical, structural, and/or geometriccharacteristics of an eye, and based on other characteristics of apatient or patient history, such as the age of a patient, medicalhistory, history of ocular procedures, life preferences, and the like.

In certain embodiments, the system 600 includes or is part aphacoemulsification system, laser treatment system, optical diagnosticinstrument (e.g, autorefractor, aberrometer, and/or corneal topographer,or the like). For example, the computer readable memory 604 mayadditionally contain instructions for controlling the handpiece of aphacoemulsification system or similar surgical system. Additionally oralternatively, the computer readable memory 604 may additionally containinstructions for controlling or exchanging data with an autorefractor,aberrometer, tomographer, and/or topographer, or the like.

Rates of spectacle independence can be predicted from through focus VA.It is better to use a combination of VA at many distances than any onedistance. Models based on combining clinical data from many studies canoffer greater understanding of potential patient outcomes, such aspredicting benefits from mini-monovision using EDOF IOLs.

The above presents a description of the best mode contemplated ofcarrying out the concepts disclosed herein, and of the manner andprocess of making and using it, in such full, clear, concise, and exactterms as to enable any person skilled in the art to which it pertains tomake and use the concepts described herein. The systems, methods anddevices disclosed herein are, however, susceptible to modifications andalternate constructions from that discussed above which are fullyequivalent. Consequently, it is not the intention to limit the scope ofthis disclosure to the particular embodiments disclosed. On thecontrary, the intention is to cover modifications and alternateconstructions coming within the spirit and scope of the presentdisclosure as generally expressed by the following claims, whichparticularly point out and distinctly claim the subject matter of theimplementations described herein.

Although embodiments have been described and pictured in an example formwith a certain degree of particularity, it should be understood that thepresent disclosure has been made by way of example, and that numerouschanges in the details of construction and combination and arrangementof parts and steps may be made without departing from the spirit andscope of the disclosure as set forth in the claims hereinafter.

As used herein, the term “processor” refers broadly to any suitabledevice, logical block, module, circuit, or combination of elements forexecuting instructions. For example, the processor 1002 can include anyconventional general purpose single- or multi-chip microprocessor suchas a Pentium® processor, a MIPS® processor, a Power PC® processor, AMD®processor, ARM processor, or an ALPHA® processor. In addition, theprocessor 602 can include any conventional special purposemicroprocessor such as a digital signal processor. The variousillustrative logical blocks, modules, and circuits described inconnection with the embodiments disclosed herein can be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.Processor 302 can be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

Computer readable memory 604 can refer to electronic circuitry thatallows information, typically computer or digital data, to be stored andretrieved. Computer readable memory 604 can refer to external devices orsystems, for example, disk drives or solid state drives. Computerreadable memory 1004 can also refer to fast semiconductor storage(chips), for example, Random Access Memory (RAM) or various forms ofRead Only Memory (ROM), which are directly connected to thecommunication bus or the processor 602. Other types of memory includebubble memory and core memory. Computer readable memory 604 can bephysical hardware configured to store information in a non-transitorymedium.

Methods and processes described herein may be embodied in, and partiallyor fully automated via, software code modules executed by one or moregeneral and/or special purpose computers. The word “module” can refer tologic embodied in hardware and/or firmware, or to a collection ofsoftware instructions, possibly having entry and exit points, written ina programming language, such as, for example, C or C++. A softwaremodule may be compiled and linked into an executable program, installedin a dynamically linked library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an erasable programmable read-only memory (EPROM). Itwill be further appreciated that hardware modules may comprise connectedlogic units, such as gates and flip-flops, and/or may comprisedprogrammable units, such as programmable gate arrays, applicationspecific integrated circuits, and/or processors. The modules describedherein can be implemented as software modules, but also may berepresented in hardware and/or firmware. Moreover, although in someembodiments a module may be separately compiled, in other embodiments amodule may represent a subset of instructions of a separately compiledprogram, and may not have an interface available to other logicalprogram units.

In certain embodiments, code modules may be implemented and/or stored inany type of computer-readable medium or other computer storage device.In some systems, data (and/or metadata) input to the system, datagenerated by the system, and/or data used by the system can be stored inany type of computer data repository, such as a relational databaseand/or flat file system. Any of the systems, methods, and processesdescribed herein may include an interface configured to permitinteraction with users, operators, other systems, components, programs,and so forth.

1. An optical system configured to select an intraocular lens (IOL) froma plurality of IOLs for manufacture or for implantation into a patienteye, the selected IOL configured or manufactured to improvepost-surgical spectacle independence outcome for the patient, the systemcomprising: a processor configured to execute programmable instructionsstored in a non-transitory computer storage medium; and a populationdatabase comprising clinical data for a plurality of patients implantedwith one of the plurality of IOLs, the clinical data comprisinginformation related to spectacle independence for a plurality of valuesof visual acuity between about −0.2 log MAR and about 1 log MAR atvarious defocus conditions between about −5D and 0D, wherein theinformation related to spectacle independence is based on responses ofthe patients implanted with one of the plurality of IOLs to aquestionnaire, wherein the processor is configured to: calculate foreach of the plurality of IOLs, a probability of being spectacleindependent for visual acuity equal to a threshold value between about−0.2 log MAR and about 1 log MAR at at least one defocus conditionsbetween about −5D and 0D based on the information related to spectacleindependence obtained from the population database; and identify one ofthe plurality of IOLs having a higher probability of being spectacleindependent for manufacture or for implantation into the patient's eye.2. The optical system of claim 1, wherein the processor is furtherconfigured to calculate for each of the plurality of IOLs, a probabilityof being spectacle independent for visual acuity equal to a thresholdvalue between about −0.2 log MAR and about 1 log MAR at at least two ormore defocus conditions between about −5D and 0D.
 3. The optical systemof claim 2, wherein the processor is further configured to assign aweight to the probability of being spectacle independent for visualacuity equal to a threshold value between about −0.2 log MAR and about 1log MAR at at least two or more defocus conditions between about −5D and0D.
 4. The optical system of claim 3, wherein the processor isconfigured to execute a machine learning algorithm to determine theweight. 5-13. (canceled)
 14. A method of manufacturing an IOLcomprising: receiving one or more parameters of an IOL selected from aplurality of IOLs based on calculating a probability of achievingspectacle independence for the plurality of IOLs from clinical dataproviding visual acuity at one or more defocus position for theplurality of IOLs in a population of patients implanted with one of theplurality of IOLs; and manufacturing the selected IOL.